pip install composio-core

composio add github

from openai import OpenAI
from composio_openai import ComposioToolSet, App

openai_client = OpenAI(api_key="******OPENAIKEY******")

# Initialise the Composio Tool Set
composio_toolset = ComposioToolSet(api_key="**\\*\\***COMPOSIO_API_KEY**\\*\\***")

## Step 4
# Get GitHub tools that are pre-configured
actions = composio_toolset.get_actions(actions=[Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER])

## Step 5
my_task = "Star a repo ComposioHQ/composio on GitHub"

# Create a chat completion request to decide on the action
response = openai_client.chat.completions.create(
model="gpt-4-turbo",
tools=actions, # Passing actions we fetched earlier.
messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": my_task}
  ]
)

npm install composio-core

import { OpenAI } from "openai";
import { OpenAIToolSet } from "composio-core";

const toolset = new OpenAIToolSet({
  apiKey: process.env.COMPOSIO_API_KEY,
});

async function setupUserConnectionIfNotExists(entityId) {
  const entity = await toolset.client.getEntity(entityId);
  const connection = await entity.getConnection('github');

  if (!connection) {
      // If this entity/user hasn't already connected, the account
      const connection = await entity.initiateConnection(appName);
      console.log("Log in via: ", connection.redirectUrl);
      return connection.waitUntilActive(60);
  }

  return connection;
}

async function executeAgent(entityName) {
  const entity = await toolset.client.getEntity(entityName)
  await setupUserConnectionIfNotExists(entity.id);

  const tools = await toolset.get_actions({ actions: ["github_activity_star_repo_for_authenticated_user"] }, entity.id);
  const instruction = "Star a repo ComposioHQ/composio on GitHub"

  const client = new OpenAI({ apiKey: process.env.OPEN_AI_API_KEY })
  const response = await client.chat.completions.create({
      model: "gpt-4-turbo",
      messages: [{
          role: "user",
          content: instruction,
      }],
      tools: tools,
      tool_choice: "auto",
  })

  console.log(response.choices[0].message.tool_calls);
  await toolset.handle_tool_call(response, entity.id);
}

executeGithubAgent("joey")

pip install --upgrade pip
pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"

from unsloth import FastLanguageModel 
from unsloth import is_bfloat16_supported
import torch
from trl import SFTTrainer
from transformers import TrainingArguments
from datasets import load_dataset
max_seq_length = 2048 # Supports RoPE Scaling internally, so choose any!
# Get LAION dataset
url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
dataset = load_dataset("json", data_files = {"train" : url}, split = "train")

# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
    "unsloth/mistral-7b-v0.3-bnb-4bit",      # New Mistral v3 2x faster!
] # More models at https://huggingface.co/unsloth

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/llama-3-8b-bnb-4bit",
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)

# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
    model,
    r = 16,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 16,
    lora_dropout = 0, # Supports any, but = 0 is optimized
    bias = "none",    # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
    random_state = 3407,
    max_seq_length = max_seq_length,
    use_rslora = False,  # We support rank stabilized LoRA
    loftq_config = None, # And LoftQ
)

trainer = SFTTrainer(
    model = model,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    tokenizer = tokenizer,
    args = TrainingArguments(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 10,
        max_steps = 60,
        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = 1,
        output_dir = "outputs",
        optim = "adamw_8bit",
        seed = 3407,
    ),
)
trainer.train()

pip3 install llmware

""" This example demonstrates creating and using datasets
    1. Datasets suitable for fine-tuning embedding models
    2. Completion and other types of datasets
    3. Generating datasets from all data in a library or with filtered data
    4. Creating datasets from AWS Transcribe transcripts
"""

import json
import os
from llmware.dataset_tools import Datasets
from llmware.library import Library
from llmware.retrieval import Query
from llmware.setup import Setup
from llmware.configs import LLMWareConfig

def build_and_use_dataset(library_name):

    # Setup a library and build a knowledge graph.  Datasets will use the data in the knowledge graph
    print (f"\n > Creating library {library_name}...")
    library = Library().create_new_library(library_name)
    sample_files_path = Setup().load_sample_files()
    library.add_files(os.path.join(sample_files_path,"SmallLibrary"))
    library.generate_knowledge_graph()

    # Create a Datasets object from library
    datasets = Datasets(library)

    # Build a basic dataset useful for industry domain adaptation for fine-tuning embedding models
    print (f"\n > Building basic text dataset...")

    basic_embedding_dataset = datasets.build_text_ds(min_tokens=500, max_tokens=1000)
    dataset_location = os.path.join(library.dataset_path, basic_embedding_dataset["ds_id"])

    print (f"\n > Dataset:")
    print (f"(Files referenced below are found in {dataset_location})")

    print (f"\n{json.dumps(basic_embedding_dataset, indent=2)}")
    sample = datasets.get_dataset_sample(datasets.current_ds_name)

    print (f"\nRandom sample from the dataset:\n{json.dumps(sample, indent=2)}")

    # Other Dataset Generation and Usage Examples:

    # Build a simple self-supervised generative dataset- extracts text and splits into 'text' & 'completion'
    # Several generative "prompt_wrappers" are available - chat_gpt | alpaca | 
    basic_generative_completion_dataset = datasets.build_gen_ds_targeted_text_completion(prompt_wrapper="alpaca")

    # Build a generative self-supervised training set by pairing 'header_text' with 'text.'
    xsum_generative_completion_dataset = datasets.build_gen_ds_headline_text_xsum(prompt_wrapper="human_bot")
    topic_prompter_dataset = datasets.build_gen_ds_headline_topic_prompter(prompt_wrapper="chat_gpt")

    # Filter a library by a key term as part of building the dataset
    filtered_dataset = datasets.build_text_ds(query="agreement", filter_dict={"master_index":1})

    # Pass a set of query results to create a dataset from those results only
    query_results = Query(library=library).query("africa")
    query_filtered_dataset = datasets.build_text_ds(min_tokens=250,max_tokens=600, qr=query_results)

    return 0

if __name__ == "__main__":

    LLMWareConfig().set_active_db("sqlite")

    build_and_use_dataset("test_txt_datasets_0")

pip install taipy

import taipy as tp
import pandas as pd
from taipy import Config, Scope, Gui

# Defining the helper functions

# Callback definition - submits scenario with genre selection
def on_genre_selected(state):
    scenario.selected_genre_node.write(state.selected_genre)
    tp.submit(scenario)
    state.df = scenario.filtered_data.read()

## Set initial value to Action
def on_init(state):
    on_genre_selected(state)

# Filtering function - task
def filter_genre(initial_dataset: pd.DataFrame, selected_genre):
    filtered_dataset = initial_dataset[initial_dataset["genres"].str.contains(selected_genre)]
    filtered_data = filtered_dataset.nlargest(7, "Popularity %")
    return filtered_data

# The main script
if __name__ == "__main__":
    # Taipy Scenario & Data Management

    # Load the configuration made with Taipy Studio
    Config.load("config.toml")
    scenario_cfg = Config.scenarios["scenario"]

    # Start Taipy Core service
    tp.Core().run()

    # Create a scenario
    scenario = tp.create_scenario(scenario_cfg)

    # Taipy User Interface
    # Let's add a GUI to our Scenario Management for a complete application

    # Get list of genres
    genres = [
        "Action", "Adventure", "Animation", "Children", "Comedy", "Fantasy", "IMAX"
        "Romance","Sci-FI", "Western", "Crime", "Mystery", "Drama", "Horror", "Thriller", "Film-Noir","War", "Musical", "Documentary"
    ]

    # Initialization of variables
    df = pd.DataFrame(columns=["Title", "Popularity %"])
    selected_genre = "Action"

    # User interface definition
    my_page = """
# Film recommendation

## Choose your favorite genre
<|{selected_genre}|selector|lov={genres}|on_change=on_genre_selected|dropdown|>

## Here are the top seven picks by popularity
<|{df}|chart|x=Title|y=Popularity %|type=bar|title=Film Popularity|>
    """

    Gui(page=my_page).run()

npm install @lancedb/lancedb

import * as lancedb from "@lancedb/lancedb";

const db = await lancedb.connect("data/sample-lancedb");
const table = await db.createTable("vectors", [
    { id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
    { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
], {mode: 'overwrite'});

const query = table.vectorSearch([0.1, 0.3]).limit(2);
const results = await query.toArray();

// You can also search for rows by specific criteria without involving a vector search.
const rowsByCriteria = await table.query().where("price >= 10").toArray();

pip install -U phidata

from phi.assistant import Assistant
from phi.llm.openai import OpenAIChat
from phi.tools.yfinance import YFinanceTools

assistant = Assistant(
    llm=OpenAIChat(model="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
    show_tool_calls=True,
    markdown=True,
)
assistant.print_response("What is the stock price of NVDA")
assistant.print_response("Write a comparison between NVDA and AMD, use all tools available.")

from phi.assistant import Assistant
from phi.tools.duckduckgo import DuckDuckGo

assistant = Assistant(tools=[DuckDuckGo()], show_tool_calls=True)
assistant.print_response("Whats happening in France?", markdown=True)

pip install arize-phoenix

import phoenix as px
session = px.launch_app()

pip install 'llama-index>=0.10.44'

import phoenix as px
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
import os
from gcsfs import GCSFileSystem
from llama_index.core import (
    Settings,
    VectorStoreIndex,
    StorageContext,
    set_global_handler,
    load_index_from_storage
)
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
import llama_index

# To view traces in Phoenix, you will first have to start a Phoenix server. You can do this by running the following:
session = px.launch_app()

# Initialize LlamaIndex auto-instrumentation
LlamaIndexInstrumentor().instrument()

os.environ["OPENAI_API_KEY"] = "<ENTER_YOUR_OPENAI_API_KEY_HERE>"

# LlamaIndex application initialization may vary
# depending on your application
Settings.llm = OpenAI(model="gpt-4-turbo-preview")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")

# Load your data and create an index. Here we've provided an example of our documentation
file_system = GCSFileSystem(project="public-assets-275721")
index_path = "arize-phoenix-assets/datasets/unstructured/llm/llama-index/arize-docs/index/"
storage_context = StorageContext.from_defaults(
    fs=file_system,
    persist_dir=index_path,
)

index = load_index_from_storage(storage_context)

query_engine = index.as_query_engine()

# Query your LlamaIndex application
query_engine.query("What is the meaning of life?")
query_engine.query("How can I deploy Arize?")

# View the traces in the Phoenix UI
px.active_session().url

pip install agentops

import agentops

# Beginning of program's code (i.e. main.py, __init__.py)
agentops.init( < INSERT YOUR API KEY HERE >)

...

# (optional: record specific functions)
@agentops.record_action('sample function being record')
def sample_function(...):
    ...

# End of program
agentops.end_session('Success')
# Woohoo You're done 🎉

# Requires Python≥3.8
pip install -U bentoml

from __future__ import annotations

import bentoml

@bentoml.service(
    resources={"cpu": "4"}
)
class Summarization:
    def __init__(self) -> None:
        import torch
        from transformers import pipeline

        device = "cuda" if torch.cuda.is_available() else "cpu"
        self.pipeline = pipeline('summarization', device=device)

    @bentoml.api(batchable=True)
    def summarize(self, texts: list[str]) -> list[str]:
        results = self.pipeline(texts)
        return [item['summary_text'] for item in results] 

pip install torch transformers  # additional dependencies for local run

bentoml serve service.py:Summarization

import bentoml

with bentoml.SyncHTTPClient('http://localhost:3000') as client:
    summarized_text: str = client.summarize([bentoml.__doc__])[0]
    print(f"Result: {summarized_text}") 

model=mistralai/Mistral-7B-Instruct-v0.1
volume=$PWD/data

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
    ghcr.io/predibase/lorax:main --model-id $model

curl 127.0.0.1:8080/generate \
    -X POST \
    -d '{
        "inputs": "[INST] Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May? [/INST]",
        "parameters": {
            "max_new_tokens": 64
        }
    }' \
    -H 'Content-Type: application/json' 

curl 127.0.0.1:8080/generate \ 
    -X POST \
    -d '{
        "inputs": "[INST] Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May? [/INST]",
        "parameters": {
            "max_new_tokens": 64,
            "adapter_id": "vineetsharma/qlora-adapter-Mistral-7B-Instruct-v0.1-gsm8k"
        }
    }' \
    -H 'Content-Type: application/json'

pip install -qU portkey-ai openai

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key=OPENAI_API_KEY,
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key=PORTKEY_API_KEY
    )
)

chat_complete = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user",
               "content": "What's a fractal?"}],
)

print(chat_complete.choices[0].message.content)

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

client = OpenAI(
    api_key=userdata.get('ANTHROPIC_API_KEY')
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="anthropic",
        api_key=PORTKEY_API_KEY
    ),
)

response = client.chat.completions.create(
    model="claude-3-opus-20240229",
    messages=[{"role": "user",
               "content": "What's a fractal?"}],
    max_tokens= 512
)

pip install litserve

# server.py
import litserve as ls

# STEP 1: DEFINE A MODEL API
class SimpleLitAPI(ls.LitAPI):
    # Called once at startup. Setup models, DB connections, etc...
    def setup(self, device):
        self.model = lambda x: x**2

    # Convert the request payload to model input.
    def decode_request(self, request):
        return request["input"]

    # Run inference on the model, and return the output.
    def predict(self, x):
        return self.model(x)

    # Convert the model output to a response payload.
    def encode_response(self, output):
        return {"output": output}

# STEP 2: START THE SERVER
if __name__ == "__main__":
    api = SimpleLitAPI()
    server = ls.LitServer(api, accelerator="auto")
    server.run(port=8000)

python server.py

python client.py